基于布谷鸟鸽群融合算法的多智能体避障研究Research on Multi-intelligent Body Obstacle Avoidance Based on the Fusion Algorithm of Pigeon Group of Cuckoo
吴波,王健安
摘要(Abstract)：
针对多智能体系统在避障过程中易出现避障路径冗长、时效性较差的问题，提出一种布谷鸟鸽群融合避障算法。首先，利用自适应调整步长策略调整布谷鸟Levy飞行步长大小，进行长、短交替搜索获得鸟巢位置更新点，得到次优避障路径；其次，在次优路径的基础上，引入鸽群优化算法(PIO)的地图罗盘算子与地表算子进行二次避障路径规划演示，再次更新位置点，进而获得最优避障路径。最后利用MATLAB仿真，通过效率函数分析对比布谷鸟人工势场算法，在20、40、60个随机分布的障碍物环境中，融合算法你避障时间缩短了约5.55%、16.00%、17.35%.
关键词(KeyWords)： 多智能体系统;避障路径规划;布谷鸟算法;鸽群优化算法
基金项目(Foundation): 山西省重点研发计划(201903D421045)
作者(Author): 吴波,王健安
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